{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# UCI Dodgers dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import os\n",
    "from pathlib import Path\n",
    "from config import data_raw_folder, data_processed_folder\n",
    "from timeeval import Datasets\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (20, 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Looking for source datasets in /home/projects/akita/data/benchmark-data/data-raw/UCI ML Repository/Dodgers and\n",
      "saving processed datasets in /home/projects/akita/data/benchmark-data/data-processed\n"
     ]
    }
   ],
   "source": [
    "dataset_collection_name = \"Dodgers\"\n",
    "source_folder = Path(data_raw_folder) / \"UCI ML Repository/Dodgers\"\n",
    "target_folder = Path(data_processed_folder)\n",
    "\n",
    "print(f\"Looking for source datasets in {source_folder.absolute()} and\\nsaving processed datasets in {target_folder.absolute()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Directories /home/projects/akita/data/benchmark-data/data-processed/univariate/Dodgers already exist\n"
     ]
    }
   ],
   "source": [
    "dataset_name = \"101-freeway-traffic\"\n",
    "train_type = \"unsupervised\"\n",
    "train_is_normal = False\n",
    "input_type = \"univariate\"\n",
    "datetime_index = True\n",
    "dataset_type = \"real\"\n",
    "\n",
    "# create target directory\n",
    "dataset_subfolder = Path(input_type) / dataset_collection_name\n",
    "target_subfolder = target_folder / dataset_subfolder\n",
    "try:\n",
    "    os.makedirs(target_subfolder)\n",
    "    print(f\"Created directories {target_subfolder}\")\n",
    "except FileExistsError:\n",
    "    print(f\"Directories {target_subfolder} already exist\")\n",
    "    pass\n",
    "\n",
    "dm = Datasets(target_folder)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Processed dataset 101-freeway-traffic -> /home/projects/akita/data/benchmark-data/data-processed/univariate/Dodgers/101-freeway-traffic.test.csv\n"
     ]
    }
   ],
   "source": [
    "data_file = source_folder / \"Dodgers.data\"\n",
    "events_file = source_folder / \"Dodgers.events\"\n",
    "\n",
    "# transform data\n",
    "df = pd.read_csv(data_file, header=None, encoding=\"latin1\", parse_dates=[0], infer_datetime_format=True)\n",
    "df.columns = [\"timestamp\", \"count\"]\n",
    "#df[\"count\"] = df[\"count\"].replace(-1, np.nan)\n",
    "\n",
    "# read and add labels\n",
    "df_events = pd.read_csv(events_file, header=None, encoding=\"latin1\")\n",
    "df_events.columns = [\"date\", \"begin\", \"end\", \"game attendance\" ,\"away team\", \"game score\"]\n",
    "df_events.insert(0, \"begin_timestamp\", pd.to_datetime(df_events[\"date\"] + \" \" + df_events[\"begin\"]))\n",
    "df_events.insert(1, \"end_timestamp\", pd.to_datetime(df_events[\"date\"] + \" \" + df_events[\"end\"]))\n",
    "df_events = df_events.drop(columns=[\"date\", \"begin\", \"end\", \"game attendance\" ,\"away team\", \"game score\"])\n",
    "# labelling\n",
    "df[\"is_anomaly\"] = 0\n",
    "for _, (t1, t2) in df_events.iterrows():\n",
    "    tmp = df[df[\"timestamp\"] >= t1]\n",
    "    tmp = tmp[tmp[\"timestamp\"] <= t2]\n",
    "    df.loc[tmp.index, \"is_anomaly\"] = 1\n",
    "# mark missing values as anomaly as well\n",
    "df.loc[df[\"count\"] == -1, \"is_anomaly\"] = 1\n",
    "\n",
    "filename = f\"{dataset_name}.test.csv\"\n",
    "path = os.path.join(dataset_subfolder, filename)\n",
    "target_filepath = os.path.join(target_subfolder, filename)\n",
    "dataset_length = len(df)\n",
    "df.to_csv(target_filepath, index=False)\n",
    "print(f\"Processed dataset {dataset_name} -> {target_filepath}\")\n",
    "\n",
    "# save metadata\n",
    "dm.add_dataset((dataset_collection_name, dataset_name),\n",
    "    train_path = None,\n",
    "    test_path = path,\n",
    "    dataset_type = dataset_type,\n",
    "    datetime_index = datetime_index,\n",
    "    split_at = None,\n",
    "    train_type = train_type,\n",
    "    train_is_normal = train_is_normal,\n",
    "    input_type = input_type,\n",
    "    dataset_length = dataset_length\n",
    ")\n",
    "\n",
    "dm.save()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>train_path</th>\n",
       "      <th>test_path</th>\n",
       "      <th>dataset_type</th>\n",
       "      <th>datetime_index</th>\n",
       "      <th>split_at</th>\n",
       "      <th>train_type</th>\n",
       "      <th>train_is_normal</th>\n",
       "      <th>input_type</th>\n",
       "      <th>length</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>collection_name</th>\n",
       "      <th>dataset_name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Dodgers</th>\n",
       "      <th>101-freeway-traffic</th>\n",
       "      <td>NaN</td>\n",
       "      <td>univariate/Dodgers/101-freeway-traffic.test.csv</td>\n",
       "      <td>real</td>\n",
       "      <td>True</td>\n",
       "      <td>NaN</td>\n",
       "      <td>unsupervised</td>\n",
       "      <td>False</td>\n",
       "      <td>univariate</td>\n",
       "      <td>50400</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                    train_path  \\\n",
       "collection_name dataset_name                     \n",
       "Dodgers         101-freeway-traffic        NaN   \n",
       "\n",
       "                                                                           test_path  \\\n",
       "collection_name dataset_name                                                           \n",
       "Dodgers         101-freeway-traffic  univariate/Dodgers/101-freeway-traffic.test.csv   \n",
       "\n",
       "                                    dataset_type  datetime_index  split_at  \\\n",
       "collection_name dataset_name                                                 \n",
       "Dodgers         101-freeway-traffic         real            True       NaN   \n",
       "\n",
       "                                       train_type  train_is_normal  \\\n",
       "collection_name dataset_name                                         \n",
       "Dodgers         101-freeway-traffic  unsupervised            False   \n",
       "\n",
       "                                     input_type  length  \n",
       "collection_name dataset_name                             \n",
       "Dodgers         101-freeway-traffic  univariate   50400  "
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dm.refresh()\n",
    "dm.df().loc[(slice(dataset_collection_name,dataset_collection_name), slice(None))]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Experimentation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>timestamp</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-04-10 00:00:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-04-10 00:05:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-04-10 00:10:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-04-10 00:15:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-04-10 00:20:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50395</th>\n",
       "      <td>2005-10-01 23:35:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50396</th>\n",
       "      <td>2005-10-01 23:40:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50397</th>\n",
       "      <td>2005-10-01 23:45:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50398</th>\n",
       "      <td>2005-10-01 23:50:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50399</th>\n",
       "      <td>2005-10-01 23:55:00</td>\n",
       "      <td>-1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>50400 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                timestamp  count\n",
       "0     2005-04-10 00:00:00     -1\n",
       "1     2005-04-10 00:05:00     -1\n",
       "2     2005-04-10 00:10:00     -1\n",
       "3     2005-04-10 00:15:00     -1\n",
       "4     2005-04-10 00:20:00     -1\n",
       "...                   ...    ...\n",
       "50395 2005-10-01 23:35:00     -1\n",
       "50396 2005-10-01 23:40:00     -1\n",
       "50397 2005-10-01 23:45:00     -1\n",
       "50398 2005-10-01 23:50:00     -1\n",
       "50399 2005-10-01 23:55:00     -1\n",
       "\n",
       "[50400 rows x 2 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_file = source_folder / \"Dodgers.data\"\n",
    "df = pd.read_csv(data_file, header=None, encoding=\"latin1\", parse_dates=[0], infer_datetime_format=True)\n",
    "df.columns = [\"timestamp\", \"count\"]\n",
    "#df[\"count\"] = df[\"count\"].replace(-1, np.nan)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>begin_timestamp</th>\n",
       "      <th>end_timestamp</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2005-04-12 13:10:00</td>\n",
       "      <td>2005-04-12 16:23:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2005-04-13 19:10:00</td>\n",
       "      <td>2005-04-13 21:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2005-04-15 19:40:00</td>\n",
       "      <td>2005-04-15 21:48:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2005-04-16 19:10:00</td>\n",
       "      <td>2005-04-16 21:52:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2005-04-17 13:10:00</td>\n",
       "      <td>2005-04-17 15:31:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>2005-09-25 13:10:00</td>\n",
       "      <td>2005-09-25 16:06:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>2005-09-26 19:10:00</td>\n",
       "      <td>2005-09-26 22:27:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>2005-09-27 19:10:00</td>\n",
       "      <td>2005-09-27 21:33:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>2005-09-28 19:10:00</td>\n",
       "      <td>2005-09-28 21:58:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>2005-09-29 19:10:00</td>\n",
       "      <td>2005-09-29 22:24:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       begin_timestamp       end_timestamp\n",
       "0  2005-04-12 13:10:00 2005-04-12 16:23:00\n",
       "1  2005-04-13 19:10:00 2005-04-13 21:48:00\n",
       "2  2005-04-15 19:40:00 2005-04-15 21:48:00\n",
       "3  2005-04-16 19:10:00 2005-04-16 21:52:00\n",
       "4  2005-04-17 13:10:00 2005-04-17 15:31:00\n",
       "..                 ...                 ...\n",
       "76 2005-09-25 13:10:00 2005-09-25 16:06:00\n",
       "77 2005-09-26 19:10:00 2005-09-26 22:27:00\n",
       "78 2005-09-27 19:10:00 2005-09-27 21:33:00\n",
       "79 2005-09-28 19:10:00 2005-09-28 21:58:00\n",
       "80 2005-09-29 19:10:00 2005-09-29 22:24:00\n",
       "\n",
       "[81 rows x 2 columns]"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "events_file = source_folder / \"Dodgers.events\"\n",
    "df_events = pd.read_csv(events_file, header=None, encoding=\"latin1\")\n",
    "df_events.columns = [\"date\", \"begin\", \"end\", \"game attendance\" ,\"away team\", \"game score\"]\n",
    "df_events.insert(0, \"begin_timestamp\", pd.to_datetime(df_events[\"date\"] + \" \" + df_events[\"begin\"]))\n",
    "df_events.insert(1, \"end_timestamp\", pd.to_datetime(df_events[\"date\"] + \" \" + df_events[\"end\"]))\n",
    "df_events = df_events.drop(columns=[\"date\", \"begin\", \"end\", \"game attendance\" ,\"away team\", \"game score\"])\n",
    "df_events"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "# labelling\n",
    "df[\"is_anomaly\"] = 0\n",
    "for _, (t1, t2) in df_events.iterrows():\n",
    "    tmp = df[df[\"timestamp\"] >= t1]\n",
    "    tmp = tmp[tmp[\"timestamp\"] <= t2]\n",
    "    df.loc[tmp.index, \"is_anomaly\"] = 1\n",
    "df.loc[df[\"count\"] == -1, \"is_anomaly\"] = 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='timestamp'>"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.iloc[15000:20000].plot(x=\"timestamp\", y=[\"count\", \"is_anomaly\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "timeeval",
   "language": "python",
   "name": "timeeval"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}
